Machine-vision-based obstacle detection and tracking is an important component in autonomous vehicle systems. In a typical driving scene, the obstacles include vehicles, pedestrians and any other objects that are either moving or rising above the road plane. The purpose of obstacle detection is to separate moving objects from the driving scene, the obstacles including vehicles, pedestrians and any other objects that are either moving or rising above the road plane. Such information is required by a number of automotive applications, e.g., adaptive cruise control, forward collision avoidance and lane departure warning. By fusing the results of detecting and tracking individual objects, it is possible to achieve sufficient perception of the driving environment.
In a monocular vision system designed for driver assistance, a single camera is mounted inside the ego-vehicle to capture image sequence of forward road scenes. Various vehicle detection methods have been developed to detect vehicles in the central field of the view. Such methods can be used in passing vehicle detection. In passing vehicle detection, vehicles that are passing the ego-vehicle upon the left or right and entering the field of view at a higher speed are detected. Passing vehicle detection plays a substantial role in understanding the driving environment. Because of the potentially unsafe driving situation that an overtaking vehicle could create, it is important to monitor and detect vehicles passing by.
Since passing vehicles need to be detected earlier on while they are entering the view and only partially visible, appearance information cannot be completely relied upon. Instead, characteristic optical flows are generated by a vehicle passing by. Hence, motion information becomes an important cue in detecting passing vehicles. Several known obstacle detection methods using optical flow have been used to detect passing vehicles. In these methods, a predicted flow field calculated from camera parameters and vehicle velocity is compared with the actual image flows calculated from motion estimation. An obstacle is declared if the actual flows do not match the predicted flows. These methods work well if neither strong noise nor illumination change is present. However, in practical situations, structured noise and strong illumination are quite common which cause spurious image features and unreliable flow estimates. There is a need for a method for detecting passing vehicles which is capable of robust motion estimation.